Timeline of heparin‐induced thrombocytopenia seroconversion in serial plasma samples tested using an automated latex immunoturbidimetric assay
Bibliographic record
Abstract
INTRODUCTION: HIT is caused by platelet-activating IgG that recognize multimolecular PF4/heparin complexes. HIT antibodies are generally detectable by PF4-dependent enzyme immunoassay (EIA) and by platelet serotonin-release assay (SRA) at the beginning of the HIT-related platelet count fall. We determined whether an automated immunoassay for HIT, the latex immunoturbidimetric assay (LIA), also detects antibodies early during the course of HIT. The LIA was also used to evaluate a patient with putative SRA-negative HIT. METHODS: We evaluated the timing and magnitude of LIA reactivity in serial plasma samples obtained from 19 SRA-positive patients (17 with abnormal platelet count changes indicating HIT; two with subclinical seroconversion) and one putative SRA-negative HIT patient, all obtained from patients who participated in a clinical trial of heparin thromboprophylaxis. We determined LIA status at the onset of the HIT-related platelet count fall. RESULTS: The LIA was positive in all 19 SRA-positive patients (median value, 7.3 U/mL [range, 1.2-35.5]; cutoff, 1.0 U/mL); for all 13 evaluable patients for whom an informative plasma sample was available at (or shortly before) the onset of the HIT-related platelet count fall, LIA reactivity was positive. Heterogeneity in seroconversion using the LIA was observed; some patients exhibited gradual increases in reactivity, whereas other patients showed rapid increase in reactivity over a few days. The single clinical trial patient who met clinical-pathological criteria for "SRA-negative HIT" tested LIA-positive. CONCLUSION: The LIA detects HIT antibodies at the beginning of the HIT-associated platelet count fall. The LIA was also positive in a patient with SRA-negative HIT.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".